Sparse recovery
Performance Analysis of STAP Algorithms Based on Fast Sparse Recovery Techniques
... and sparse recovery (SR) techniques used in radar, several authors have considered CS and SR ideas for moving target indication (MTI) and STAP problems, such as sparse-recovery-based STAP type ...
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Deterministic aided single dataset STAP method based on sparse recovery in heterogeneous clutter environments
... be large when facing a severe heterogeneous environ- ment. Moreover, several non-homogeneity detection (NHD) algorithms [11–15] have been applied in the het- erogeneous environments, such as the power-selected training ...
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Enhanced TWI Under Wall Parameter Uncertainty with the Parametric Sparse Recovery Method
... Abstract—In recent years, through-wall imaging (TWI) has gained much research interest because of urgent needs of civilian, security, and defense applications. TWI based on compressive sensing (CS) method can produce ...
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Noisy Sparse Recovery Based on Parameterized Quadratic Programming by Thresholding
... pattern recovery requires simulta- neous recovery of nonzero entries as well as zero ...entry recovery and zero entry recovery ...entry recovery, a large h is always ...pattern ...
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Two New Approaches to Compressed Sensing Exhibiting Both Robust Sparse Recovery and the Grouping Effect
... Now we place these contributions in perspective. There is empirical evidence to support the belief that both the GL and the SGL formulations work well for compressed sensing. However, until the publication of a companion ...
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An Agile and Robust Sparse Recovery Method for MR Images Based on Selective k-space Acquisition and Artifacts Suppression
... Abstract—Magnetic Resonance Imaging (MRI) has some attractive advantages over other medical imaging techniques. Its widespread application as a medical diagnostic tool is however hindered by its long acquisition time as ...
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Practical security and privacy attacks against biometric hashing using sparse recovery
... Biometric hashing is a cancelable biometric verification method that has received research interest recently. This method can be considered as a two-factor authentication method which combines a personal password (or ...
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Sparsity-Based Multi-Target Direct Positioning Algorithm Based on Joint-Sparse Recovery
... In this paper, we investigate a formulation of the multi-target direct localization problem in the SR framework. We exploit the joint-sparse property to present a novel sparsity-based DPD algorithm as opposed to ...
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Sparse Recovery via Convex Optimization
... While the uniform uncertainty results are impressive because they hold for all suffi- ciently sparse x, they also have drawbacks. For example, while it is possible to show that a matrix drawn from one of the ...
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Model-based sparse recovery method for automatic classification of helicopters
... A sparse signal model for radar return from a helicopter was designed and used in conjunction with a greedy sparse signal recovery algorithm to extract the micro-Doppler parameters of the ...
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Space-Time Adaptive Processing Based on Weighted Regularized Sparse Recovery
... L1-regularized STAP filters in [16, 17], etc.. The basic idea of SR- STAP type algorithms is to regularize a linear inverse problem by including prior knowledge that the clutter spectrum is sparse in the ...
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Exact recovery of sparse multiple measurement vectors by \(l {2,p}\) minimization
... joint sparse recovery problem is a generalization of the single measurement vector problem widely studied in compressed ...jointly sparse vectors, ...
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Subspace weighted ℓ2,1 minimization for sparse signal recovery
... 4.4 High resolution radar imaging via sparse recovery Here we attempt to obtain high range resolution in data collected by a real stepped frequency radar. The radar is Ka band and the frequency step size Δf ...
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Noisy Sparse Subspace Clustering
... for sparse recovery, and therefore noisy SSC requires analyzing a noisy ...structure recovery for noisy data where stability/perturbation bounds are ...
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Comparative Study on Sparse and Recovery Algorithms for Antenna Measurement by Compressed Sensing
... sensing/sparse recovery theory and its usefulness in practical electromagnetic problems ...for sparse test results, allowing for efficient and easy antenna measurements ...
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Adaptively Sparse Transformers
... weights. Sparse attention may mitigate these issues; however, our work focuses mostly on a more mechanical aspect of interpretation by analyzing head behavior, rather than on explanations for ...
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Recovering sparse graphs
... The work presented in this paper is motivated by the line of research on algorithmic metatheorems, general algorithmic results that guarantee the existence of efficient algorithms for wide classes of problems. The most ...
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Feature Selection in Sparse Matrices
... Abstract Feature selection, as a pre-processing step to machine learning, is effective in reducing dimensionality, removing irrelevant data, increasing learning accuracy, and improving result comprehensibility. There are ...
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Sparse Grid Interpolation
... the sparse grid interpolation to reduce the curse of dimensionality. A sparse adaptive surrogate model is constructed for the Hokkaido-Nansei-Oki tsunami, for which we give a description in Section ...of ...
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From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images
... of sparse representations for signals and images. Although sparse representation is a poorly defined problem and a computationally im- practical goal in general, we have pointed to mathematical results ...
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